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The Bhargava Greedoid As a Gaussian Elimination Greedoid

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The Bhargava Greedoid As a Gaussian Elimination Greedoid The Bhargava greedoid as a Gaussian elimination greedoid Darij Grinberg* June 10, 2021 Abstract. Inspired by Manjul Bhargava’s theory of generalized facto- rials, Fedor Petrov and the author have defined the Bhargava greedoid – a greedoid (a matroid-like set system on a finite set) assigned to any “ul- tra triple” (a somewhat extended variant of a finite ultrametric space). Here we show that the Bhargava greedoid of a finite ultra triple is always a Gaussian elimination greedoid over any sufficiently large (e.g., infinite) field; this is a greedoid analogue of a representable matroid. We find necessary and sufficient conditions on the size of the field to ensure this. Contents 1. Gaussian elimination greedoids4 1.1. The definition . .4 1.2. Context . .5 2. V-ultra triples6 3. The main theorem9 4. Cliques and stronger bounds 10 5. The converse direction 11 6. Valadic V-ultra triples 12 7. Isomorphism 25 8. Decomposing a V-ultra triple 26 *Drexel University, Korman Center, Room 291, 15 S 33rd Street, Philadelphia PA, 19104, USA 1 The Bhargava greedoid as a Gaussian elimination greedoid page 2 9. Valadic representation of V-ultra triples 30 10.Proof of the main theorem 36 11.Proof of Theorem 5.1 36 11.1. Closed balls . 36 11.2. Exchange results for sets intersecting a ball . 39 11.3. Gaussian elimination greedoids in terms of determinants . 42 11.4. Proving the theorem . 48 12.Appendix: Gaussian elimination greedoids are strong 51 13.Appendix: Proof of Proposition 1.7 55 14.Appendix: Proofs of some properties of L 58 *** The notion of a greedoid was coined in 1981 by Korte and Lóvász, and has since seen significant developments ([KoLoSc91], [BjoZie92]). It is a type of set system (i.e., a set of subsets of a given ground set) that is required to satisfy some axioms weaker than the matroid axioms – so that, in particular, the independent sets of a matroid form a greedoid. In [GriPet19], Fedor Petrov and the author have constructed a greedoid stem- ming from Bhargava’s theory of generalized factorials, albeit in a setting signifi- cantly more general than Bhargava’s. Roughly speaking, the sets that belong to this greedoid are subsets of maximum perimeter (among all subsets of their size) of a finite ultrametric space. More precisely, our setup is more general than that of an ultrametric space: We work with a finite set E, a distance function d that as- signs a “distance” d (e, f ) to any pair (e, f ) of distinct elements of E, and a weight function w that assigns a “weight” w (e) to each e 2 E. The distances and weights are supposed to belong to a totally ordered abelian group V (for example, R). The distances are required to satisfy the symmetry axiom d (e, f ) = d ( f , e) and the “ul- trametric triangle inequality” d (a, b) 6 max fd (a, c) , d (b, c)g. In this setting, any subset S of E has a well-defined perimeter, obtained by summing the weights and the pairwise distances of all its elements. The subsets S of E that have maximum perimeter (among all jSj-element subsets of E) then form a greedoid, which has been called the Bhargava greedoid of (E, w, d) in [GriPet19]. This greedoid is further- more a strong greedoid [GriPet19, Theorem 6.1], which implies in particular that for any given k 6 jEj, the k-element subsets of E that have maximum perimeter are the bases of a matroid. In the present paper, we prove that the Bhargava greedoid of (E, w, d) is a Gaus- sian elimination greedoid over any sufficiently large (e.g., infinite) field (a greedoid The Bhargava greedoid as a Gaussian elimination greedoid page 3 analogue of a representable matroid1). We quantify the “sufficiently large” by pro- viding a sufficient condition for the size of the field. When all weights w (e) are equal, we show that this condition is also necessary. We note that the Bhargava greedoid can be seen to arise from an optimization problem in phylogenetics: Given a finite set E of organisms and an integer k 2 N, we want to choose a k-element subset of E that maximizes some kind of biodi- versity. Depending on the definition of biodiversity used, the properties of the maximizing subsets can differ. It appears natural to define biodiversity in terms of distances on the evolutionary tree (which is a finite ultrametric space), and such a definition has been considered by Moulton, Semple and Steel in [MoSeSt06], leading to the result that the maximum-biodiversity sets form a strong greedoid. The Bhargava greedoid is an analogue of their greedoid using a slightly different definition of biodiversity2. The present paper potentially breaks this analogy by showing that the Bhargava greedoid is a Gaussian elimination greedoid, whereas this is unknown for the greedoid of Moulton, Semple and Steel. Whether the latter is a Gaussian elimination greedoid as well remains to be understood3, as does the question of interpolating between the two notions of biodiversity. This paper is self-contained (up to some elementary linear algebra), and in par- ticular can be read independently of [GriPet19]. The 12-page extended abstract [GriPet20] summarizes the highlights of both [GriPet19] and this paper; it is thus a convenient starting point for a reader in- terested in the subject. Acknowledgments I thank Fedor Petrov for his (major) part in the preceding project [GriPet19] that led to this one. 1In particular, this entails that all the matroids mentioned in the preceding paragraph are repre- sentable. 2To be specific: We view the organisms as the leaves of an evolutionary tree T that obeys a molecular clock assumption (i.e., all its leaves have the same distance from the root). Then, the set E of these organisms is equipped with a distance function (measuring distances along the edges of the tree), which satisfies the “ultrametric triangle inequality”. We define the weight function w by setting w (e) = 0 for all e 2 E. Now, the phylogenetic diversity of a subset S ⊆ E is defined to be the sum of the edge lengths of the minimal subtree of T that connects all leaves in S. This phylogenetic diversity is the measure of biodiversity used in [MoSeSt06]. Meanwhile, our notion of perimeter can also be seen as a measure of biodiversity – perhaps even a better one for sustainability questions, as it rewards subsets that are roughly balanced across different clades. To give a trivial example, a zoo optimized using phylogenetic diversity might have dozens of mammals and only one bird, while this would unlikely be considered optimal in terms of perimeter. The molecular clock assumption can actually be dropped, at the expense of changing the weight function to account for different distances from the root. 3This question might have algorithmic significance. At least for polymatroids, representability can make the difference between a problem being NP-hard and in P, as shown by Lóvasz in [Lovasz80] for polymatroid matching. The Bhargava greedoid as a Gaussian elimination greedoid page 4 This work has been started during a Leibniz fellowship at the Mathematisches Forschungsinstitut Oberwolfach, and completed at the Institut Mittag-Leffler in Djursholm; I thank both institutes for their hospitality. This material is based upon work supported by the Swedish Research Council under grant no. 2016-06596 while the author was in residence at Institut Mittag-Leffler in Djur- sholm, Sweden during Spring 2020. 1. Gaussian elimination greedoids 1.1. The definition Convention 1.1. Here and in the following, N denotes the set f0, 1, 2, . .g. Convention 1.2. If E is any set, then 2E will denote the powerset of E (that is, the set of all subsets of E). Convention 1.3. Let K be any field, and let n 2 N. Then, Kn shall denote the K-vector space of all column vectors of size n over K. We recall the definition of a Gaussian elimination greedoid: Definition 1.4. Let E be a finite set. Let m 2 N be such that m > jEj. Let K be a field. For each k 2 f0, 1, . , mg, let p : Km ! Kk be the projection map that removes all but the first k coordinates k 0 1 0 1 0 1 a1 a1 a1 B a C B a C B a C B 2 C B 2 C B 2 C m of a column vector. (That is, pk B . C = B . C for each B . C 2 K .) @ . A @ . A @ . A am ak am m For each e 2 E, let ve 2 K be a column vector. The family (ve)e2E will be called a vector family over K. Let G be the subset F jFj F ⊆ E j the family pjFj (ve) 2 K is linearly independent e2F of 2E. Then, G is called the Gaussian elimination greedoid of the vector family (ve)e2E. It is furthermore called a Gaussian elimination greedoid on ground set E. Example 1.5. Let K = Q and E = f1, 2, 3, 4, 5g and m = 6. Let v1, v2, v3, v4, v5 2 K6 be the columns of the 6 × 5-matrix 0 0 1 1 0 1 1 B 1 1 0 0 0 C B C B 0 2 1 0 1 C B C . B 1 0 1 0 0 C B C @ 0 0 0 0 0 A 1 2 0 2 1 The Bhargava greedoid as a Gaussian elimination greedoid page 5 Then, the Gaussian elimination greedoid of the vector family (ve)e2E = (v1, v2, v3, v4, v5) is the set f?, f2g , f3g , f5g , f1, 2g , f1, 3g , f1, 5g , f2, 3g , f2, 5g , f1, 2, 3g , f1, 2, 5g , f1, 2, 3, 5gg .
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